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Flow estimation solely from image data through persistent homology analysis
Topological data analysis is an emerging concept of data analysis for characterizing shapes. A state-of-the-art tool in topological data analysis is persistent homology, which is expected to summarize quantified topological and geometric features. Although persistent homology is useful for revealing...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8429714/ https://www.ncbi.nlm.nih.gov/pubmed/34504173 http://dx.doi.org/10.1038/s41598-021-97222-6 |
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author | Suzuki, Anna Miyazawa, Miyuki Minto, James M. Tsuji, Takeshi Obayashi, Ippei Hiraoka, Yasuaki Ito, Takatoshi |
author_facet | Suzuki, Anna Miyazawa, Miyuki Minto, James M. Tsuji, Takeshi Obayashi, Ippei Hiraoka, Yasuaki Ito, Takatoshi |
author_sort | Suzuki, Anna |
collection | PubMed |
description | Topological data analysis is an emerging concept of data analysis for characterizing shapes. A state-of-the-art tool in topological data analysis is persistent homology, which is expected to summarize quantified topological and geometric features. Although persistent homology is useful for revealing the topological and geometric information, it is difficult to interpret the parameters of persistent homology themselves and difficult to directly relate the parameters to physical properties. In this study, we focus on connectivity and apertures of flow channels detected from persistent homology analysis. We propose a method to estimate permeability in fracture networks from parameters of persistent homology. Synthetic 3D fracture network patterns and their direct flow simulations are used for the validation. The results suggest that the persistent homology can estimate fluid flow in fracture network based on the image data. This method can easily derive the flow phenomena based on the information of the structure. |
format | Online Article Text |
id | pubmed-8429714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84297142021-09-13 Flow estimation solely from image data through persistent homology analysis Suzuki, Anna Miyazawa, Miyuki Minto, James M. Tsuji, Takeshi Obayashi, Ippei Hiraoka, Yasuaki Ito, Takatoshi Sci Rep Article Topological data analysis is an emerging concept of data analysis for characterizing shapes. A state-of-the-art tool in topological data analysis is persistent homology, which is expected to summarize quantified topological and geometric features. Although persistent homology is useful for revealing the topological and geometric information, it is difficult to interpret the parameters of persistent homology themselves and difficult to directly relate the parameters to physical properties. In this study, we focus on connectivity and apertures of flow channels detected from persistent homology analysis. We propose a method to estimate permeability in fracture networks from parameters of persistent homology. Synthetic 3D fracture network patterns and their direct flow simulations are used for the validation. The results suggest that the persistent homology can estimate fluid flow in fracture network based on the image data. This method can easily derive the flow phenomena based on the information of the structure. Nature Publishing Group UK 2021-09-09 /pmc/articles/PMC8429714/ /pubmed/34504173 http://dx.doi.org/10.1038/s41598-021-97222-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Suzuki, Anna Miyazawa, Miyuki Minto, James M. Tsuji, Takeshi Obayashi, Ippei Hiraoka, Yasuaki Ito, Takatoshi Flow estimation solely from image data through persistent homology analysis |
title | Flow estimation solely from image data through persistent homology analysis |
title_full | Flow estimation solely from image data through persistent homology analysis |
title_fullStr | Flow estimation solely from image data through persistent homology analysis |
title_full_unstemmed | Flow estimation solely from image data through persistent homology analysis |
title_short | Flow estimation solely from image data through persistent homology analysis |
title_sort | flow estimation solely from image data through persistent homology analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8429714/ https://www.ncbi.nlm.nih.gov/pubmed/34504173 http://dx.doi.org/10.1038/s41598-021-97222-6 |
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